Workflow Templates

Use case This workflow automatically qualifies great leads from a form and sends them an email 😮.. It also adds the user to Hubspot if not already added and records the outreach. How to setup Add you MadKudu, Hunter, and Gmail credentials Setup your HubSpot Oauth2 creds using n8n docs Set the email content and subject Click the Test Workflow button, enter your email and check the Slack channel Activate the workflow and use the form trigger production URL to collect your leads in a smart way How to adjust this template You may want to raise or lower the threshold for your leads, as you see fit. You also need to update the content (the email and the subject), obviously 😅.

🚀 AI-Powered YouTube Video Summary Distributor: From Channel to Community! Workflow Overview This sophisticated n8n automation transforms YouTube content discovery into a seamless, multi-platform intelligence sharing process. By intelligently connecting YouTube RSS, AI summarization, and content distribution platforms, the workflow: Discovers New Content: Monitors YouTube channels via RSS feed Captures latest video uploads Tracks content in real-time AI-Powered Summarization: Extracts video metadata Generates concise, meaningful summaries Leverages GPT-4o for intelligent content analysis Intelligent Distribution: Logs summaries in Google Sheets Sends summaries to Slack for review Publishes approved content to Reddit Detailed Setup Instructions 1. YouTube Data API Configuration Prerequisites Google Cloud Console account Enabled YouTube Data API v3 Setup Steps: Go to Google Cloud Console Create a new project Enable YouTube Data API v3 Create credentials (API Key) Store API key securely in n8n credentials Obtain channel RSS feed URL 2. OpenAI API Setup Prerequisites OpenAI account Active API subscription Configuration: Visit OpenAI Platform Generate API key Select GPT-4o model Configure API key in n8n credentials Set up billing and usage limits 3. Slack Integration Prerequisites Slack workspace Slack app permissions Setup Process: Create a Slack app in your workspace Configure OAuth scopes for sending messages Install app to workspace Obtain webhook or OAuth token Configure in n8n Slack node 4. Reddit API Configuration Prerequisites Reddit account Reddit application created Steps: Go to Reddit's app preferences Create a new application Obtain client ID and secret Configure OAuth2 credentials in n8n Select target subreddit Workflow Customization Channel Modification Replace YouTube RSS feed URL in trigger node Adjust channel_id parameter Modify extraction logic if needed Subreddit Customization Change subreddit parameter in Reddit node Adjust title and text formatting AI Summarization Tuning Modify system message in Summarizer Agent Adjust prompt for different content types Implement custom filtering Key Customization Points Modify RSS feed URL Change target subreddit Adjust AI summarization prompt Add custom filtering logic Implement multi-channel support Technical Requirements n8n v0.220.0 or higher YouTube Data API v3 OpenAI API access Slack workspace Reddit application Stable internet connection Potential Use Cases Content creator content tracking Research and trend analysis Social media content distribution Automated content curation Community engagement Security Considerations Use environment variables for API keys Implement proper OAuth2 authentication Respect platform usage guidelines Maintain user privacy Future Enhancement Roadmap Multi-language support Advanced content filtering Sentiment analysis integration Expanded platform distribution Customizable summarization parameters Workflow Visualization [YouTube RSS Trigger] ⬇️ [Extract Channel ID] ⬇️ [Fetch Video Details] ⬇️ [AI Summarization] ⬇️ [Google Sheets Logging] ⬇️ [Slack Approval] ⬇️ [Reddit Publishing] Hashtag Performance Boost 🚀 #YouTubeAutomation #AIContentDistribution #WorkflowInnovation #ContentCuration #AIMarketing #DigitalMediaTech #AutomatedSummaries #CrossPlatformContent Connect With Me Ready to revolutionize your content workflow? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your content strategy with intelligent, automated workflows! Note: Always test and customize the workflow to fit your specific use case and comply with platform guidelines.

Automated outreach system that identifies and contacts potential leads from CrunchBase with personalized, timely messages. 🚀 What It Does Identifies target companies and contacts Personalizes email content Schedules follow-ups Tracks responses Integrates with email providers 🎯 Perfect For Sales development reps Business development teams Startup founders Investment professionals Partnership managers ⚙️ Key Benefits ✅ Automated lead generation ✅ Personalized outreach at scale ✅ Follow-up automation ✅ Response tracking ✅ Time-saving workflow 🔧 What You Need CrunchBase API access Email service (e.g., Gmail, SendGrid) n8n instance CRM (optional) 📊 Features Contact information extraction Email template personalization Send time optimization Open/click tracking Response handling 🛠️ Setup & Support Quick Setup Start sending in 30 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Transform your outbound sales process with automated, personalized outreach to high-quality leads from CrunchBase.

Who is this for? This workflow is designed for Customer Satisfaction Managers (CSM), sales professionals, and operations managers who need to automate the analysis of client transcripts, save summarized notes to HubSpot, and route relevant feedback to the appropriate departments via email. What problem is this workflow solving? / Use Case Manually processing client conversations, extracting key insights, and distributing them to the right teams is time-consuming and error-prone. This workflow automates: Transcript analysis** using AI (OpenAI) to identify relevant content. HubSpot integration** to log meeting notes against client records. Email routing** to ensure feedback reaches the correct departments (e.g., support, sales, product, admin). What this workflow does Input Transcript: Accepts a client conversation transcript (e.g., from emails, calls, or chats). HubSpot Sync: Searches for the client’s HubSpot ID using their email. Uploads a summarized version of the conversation as meeting notes. AI-Powered Routing: Uses an OpenAI model to analyze the transcript and categorize content by department. Triggers emails (via Gmail) to route feedback to the relevant teams. Form Completion: Ends the workflow with optional user confirmation. Setup Prerequisites: n8n instance (cloud or self-hosted). HubSpot API credentials (for contact lookup and notes upload). OpenAI API key (for transcript analysis). Gmail account (for sending emails). Configuration: Replace placeholder nodes (e.g., HubSpot, OpenAI, Gmail) with your authenticated accounts. Define email templates and recipient addresses for routing. Adjust the OpenAI prompt to match your categorization criteria (e.g., "support," "billing"). How to customize this workflow to your needs Transcript Sources**: Extend the workflow to pull transcripts from other sources (e.g., Zoom, Slack). Departments**: Modify the routing logic to include additional teams or conditions. Notifications**: Add Slack/MS Teams alerts for urgent feedback. Error Handling**: Introduce retries or fallback actions for failed HubSpot/Gmail steps.

This template is based on the following template. Thank you for the groundwork, Matheus. How it works: Store your snippets of text in a Notion table. Each snippet should have an image associated with it (copy + pasted into the text) Connect to your table via a Notion "integration", from which N8N can then query your pre-meditated posts The text is fed through an OpenAI assistant to boost engagement via formatting The re-formatted text along with the image pulled from the Notion snippet are combined into a post for your LinkedIn The row in the original Notion table from step 1 containing this post is set to a status of "Done" Set up steps: You will need to create a Notion "integration", which will yield a "secret key" which you enter into your N8N as a "Credential". You will need to create a LinkedIn "app" in order to post on your behalf. When creating your LinkedIn "app", you will be required to link this "app" to a company page on LinkedIn. If you are doing this for yourself, seach for the "Default Company Payge (for API testing)", and select this page as it is provided by LinkedIn for individuals. You can find your LinkedIn apps here, and if you get stuck, further instructions on setting up this workflow (including this LinkedIn OAuth piece) can be found in this YouTube Video Aide to these instructions. Lastly, you will need to create an OpenAI API key, found on your OpenAI Playground Dashboard. Once you created an API key, make sure you have an assistant created from the "Assistants" tab on the OpenAI dashboard. This assistant and its instructions will be needed for carrying out the re-formatting of your post.

This n8n template demonstrates how to use AI to score the all Resumes by matching it with Job profile Problem Statement: A Hr person is flooded with resume and spends hours manually checking each to find most suitable ones. How it works It is linked to Gmail Trigger which upon receving any mail with specific subject will check for the attachment. Attachment will be parsed to understand the resume Candidate informtion will be broken into Personal, Eductional and Professional type Job profile will be pulled from Notion Board A HR expert powered by Gemini LLM will score each profile on basis on its relevancy Information will be updated back to Gsheet Message lable will be updated back for clarity How to use The gmail trigger node is used as an example but feel free to replace this with other triggers such as webhook or even a form. Requirements Gemini account for LLM Google sheet for upload Gmail as trigger Llama parse credentials

This n8n template demonstrates how to calculate the evaluation metric "Similarity" which in this scenario, measures the consistency of the agent. The scoring approach is adapted from the open-source evaluations project RAGAS and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py How it works This evaluation works best where questions are close-ended or about facts where the answer can have little to no deviation. For our scoring, we generate embeddings for both the AI's response and ground truth and calculate the cosine similarity between them. A high score indicates LLM consistency with expected results whereas a low score could signal model hallucination. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

How it works Automates the retrieval of Calvin and Hobbes daily comics. Extracts the comic image URL from the website. Translates comic dialogues to English and Korean. Posts the comic and translations to Discord daily. Set up steps Estimated setup time: ~10-15 minutes. Use a Schedule Trigger to automate the workflow at 9 AM daily. Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord. Add detailed notes to each node in the workflow for easy understanding.

This workflow demonstrates a simple way to run evals on a set of test cases stored in a Google Sheet. The example we are using comes from an info extraction task dataset, where we tested 6 different LLMs on 18 different test cases. This workflow extends the functionality of my simple eval for benchmarking legal tasks here. Rather than running executions sequentially (waiting for each one to respond before making another request), we use parallel processing to fire 2 requests every second. You can see our sample data in this spreadsheet here to get started. Once you have this working for our dataset, you can plug in your own test cases matching different LLMs to see how it works with your own data. How it works Pull our test cases from Google Sheets. For each case, fire off an HTTP request to a webhook. That webhook grabs the relevant source file from Google Drive and converts it to text. The text gets sent to an LLM via Open Router (so we can easily swap out models). Results come back and are logged in Google Sheets. Set up steps: Add your credentials for Google Sheets, Google Drive, and OpenRouter. Make a copy of the original data spreadsheet so that you can edit it yourself. You will need to plug your version in the Update Results node to see the spreadsheet update on each run of the loop.

This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs Add custom instructions to your Agent by changing the prompts in it. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. Describe your data properly in the Q&A tool Exchange the Simple Vector Store nodes with your own vector store tools ready for production. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.

It allows you to automate candidate retrieval and onboarding in your HR processes. How it works It monitors a Gmail address for new emails with a PDF attachment It expects the PDF to be a candidate’s CV, extracts the text using OCR, and then structures the data using ChatGPT Once the data is processed, it connects to Notion and adds (or updates) an entry in the specified database How to use Configure your Gmail account and provide your ChatGPT API key Provide an API key for the OCR service in a variable named OCR_SPACE_API_KEY Connect your Notion account Once everything is configured, the workflow will monitor your inbox for new emails. Just send an email with a PDF attachment to the configured address Requirements In addition to Gmail, ChatGPT, and Notion, the system uses a third-party OCR API (OCR SPACE). You’ll need to create an account and obtain an API key You must map the fields returned by ChatGPT to the Notion database, or use the same field names we are using Customising It should be easy to replace Notion with PostgreSQL or another database if needed

Automatically classify incoming leads based on the sentiment of their message using Google Gemini, store them in Supabase by category, and send tailored WhatsApp messages via the official WhatsApp Cloud API. ✅ Use Case: This workflow is ideal for sales, onboarding, and customer support teams who want to: Understand the tone and urgency of each lead Prioritize hot leads instantly Send smart, automatic WhatsApp replies based on user sentiment 🧠 How it works: Capture lead via a Typeform webhook Clean and structure the data (name, email, message, etc.) Run sentiment analysis using Google Gemini to classify the message as: Positive → Hot Lead Neutral → Warm Lead Negative → Cold Lead Store lead data in Supabase under the corresponding category Merge data to unify flow paths Send WhatsApp message using the official WhatsApp Cloud API, with a custom reply for each sentiment result 🔧 Tools used: Typeform (incoming data) Google Gemini (AI-based sentiment classification) Supabase (database) WhatsApp Cloud API (response automation) 🏷 Tags: AI, Sentiment Analysis, Lead Qualification, Supabase, WhatsApp, Gemini, Typeform, CRM, Automation, Customer Engagement